This page assembles some core ideas about building a more innovative local ecology. If you just bumped into this page (like a visitor from another planet), you may not understand the vocabulary. To fix that, you need to get some background from my negotiation and leadership materials.
1. Why are ecologies important?
1.1 Why getting big got important and why getting smart is more important now
Thinking about Tom Peters’ complaint about big companies. why less innovative?
example - DuPont, GM
Thinking about Dudik’s Strategic Renaissance - as a competition based model - that’s why you need a hammer rather than lots of pivots
the bet is that managed conversation is a more efficient tool to produce innovation than putting “knowledge workers” in cubicles to stare at the wall. from tesla post
1.2 Is being smart an individual or group activity?
the trend towards understanding the value of conversation - Johnson
Post - Kaku and boring science
1.3 What is the effect of locality on group activity?
what works better when we are together? thinking about coworking space
1.4. What defines the locality where we operate?
we need to put together the elements needed to make locality productive (rather than relying on other people’s learning without an idea of next steps)
2. What metrics define effective and ineffective ecologies?
2.1 A reminder - we create our own metrics
from the leadership model
2.2 Effective metrics building as a learning exercise
the value of the laboratory idea
2.3 What goes into the learning curve?
stealing the lab dynamic to produce learning on main street
3. What factors improve/destroy ecologies
3.1 Johnson versus Logan
3.2 Rules versus self generation - self-generating standards?
3.3 Partnering as a goal or a problem
4. How does one build a more effective ecology?
4.1 Where to start?
people or ideas?
4.2 What institutional arrays?
are we prisoners of our projects?
4.3 What procedures?
communication loops and tracking
4.4 What people skills?
4.5 Taking a long term view (staging - tracking capacity, building target groups as network anchors, identifying learning threads, measuring results against the model)
5. Case studies on ecology building